Tobias Wietelmann | DataTideHH
I am currently retraining as a Fachinformatiker für Daten- und Prozessanalyse (IHK, expected completion 06/2027) and building a practical portfolio around SQL, Power BI, Python, data quality, process analysis and the Microsoft Data Stack.
I am seeking a mandatory internship in Hamburg beginning on 9 September 2026, ideally in Data/BI, process analysis, reporting or a Microsoft-oriented data environment.
My previous background in PR, communication, international business contexts, documentation and project-oriented stakeholder work helps me connect technical data work with business needs and clear decision support.
Professional direction
| Area | Practical focus |
|---|---|
| Data & BI | KPIs, reporting logic, dashboards and business questions |
| SQL & data modelling | Relational structures, joins, aggregation and data quality |
| Python data workflows | API collection, CSV/JSON processing, pandas and reproducible analysis |
| Microsoft Data Stack | Power BI, SQL Server, Fabric perspective and Azure fundamentals |
| Process analysis | Translating operational workflows into data models and measurable evidence |
| Communication | Explaining technical findings to business and technical stakeholders |
Selected Data / BI projects
| Project | Evidence |
|---|---|
| Fabric Service Operations Analytics | Tested local pipeline on deterministic synthetic service-operations data: Bronze/Silver/Gold Parquet layers, DuckDB SQL marts, metric contracts and SLA breach analysis. Microsoft Fabric and Power BI mapping planned, not implemented |
| Service Operations AI Orchestration | Java 21 / Spring Boot / Spring AI companion that exposes the pinned analytics snapshot through two read-only tools, keeping KPI definitions and interpretation boundaries outside the model. Deterministic checks implemented, live-provider evaluation prepared but not yet executed |
| Excel to SQL Data Workflows | Equivalent analytical intent across Excel, Power Query M, SQL Server T-SQL and Power BI DAX, backed by deterministic data, a generated workbook, 19 tests and Windows/Linux CI |
| Flask Country Data API | Reproducible World Bank ingestion, constrained SQLite persistence, SQL data-quality checks, OpenAPI and cross-platform CI |
| Network Operations Data Lab | Sanitized infrastructure records connected to Python, SQL, data quality and BI-style reporting |
| Music Production Data Lab | Relational model, reproducible Python/SQLite workflow, reporting views and a documented Power BI semantic model |
| Hamburg District Data Basics | Public Hamburg district data, exploratory analysis and dashboard preparation |
| Open-Meteo Germany Weather Ranking | API/JSON workflow, scoring logic, CSV output and automated tests |
| SQL Server Docker Basics | SQL Server 2022, Docker, SQL scripts and reporting-oriented database practice |
Supporting technical foundations
| Project | Evidence |
|---|---|
| Cisco Switching Lab | Physical IOS lab, secure administration, lifecycle awareness and CCNA-oriented verification |
| IPv4 Subnet Calculator Multilang | One IPv4/CIDR contract implemented in Java, C++ and Python with shared tests |
| Spring Boot Process API Basics | Small layered Java REST API with validation and persistence |
| Remote Access Network Lab | Public-safe Tailscale and SSH access model with trust and offboarding documentation |
This supporting breadth complements the main Data/BI direction. It is not presented as a claim of separate network-engineering or backend specialization.
How I work
- define the business or process question first
- document sources, assumptions and scope boundaries
- keep data models explainable
- validate data before interpretation
- prefer reproducible workflows over one-off manual steps
- use readable SQL and Python
- connect technical implementation with stakeholder needs
- use AI-assisted tools as support, not as a substitute for understanding
Selected writing
- The Dashboard Wasn’t the Hard Part: Why KPI Definitions Matter — a project-based article on KPI semantics, denominator choices, aggregation, data quality and validation, based on Fabric Service Operations Analytics.
- Hiring Is a Prediction Problem: What Credential Inflation Changes — an essay on hiring as a prediction problem and why credential inflation can reduce differentiation while gatekeeping and informational value remain.
Portfolio links
DataTideHH is my personal portfolio and project label. It is not presented as an agency, consulting firm or cloud company.